chore(P3): baseline 落库下沉 + MatchPredictPanel 拆分 + 多 worker/CSRF 文档
P3-2 baseline 落库从路由下沉到服务层(predict_baseline 内直接落库),
删除路由层 _persist_baseline,三种模式统一 result.prediction_id,对外 JSON 不变。
P3-1 MatchPredictPanel.PredictionPanel 拆为 OutcomePanel/AgentsPanel/ReasoningPanel
三个子组件,本文件保留 PredictModal/PredictProgress/Spinner,对外导出路径不变。
P3-3 docs 加 ⚠️ 多 worker 陷阱红字 + STRICT_SINGLE_WORKER 环境变量(启动期强制拒绝多 worker)。
P3-4 docs 新增「同站部署 vs 跨站 CSRF」节。
This commit is contained in:
shangfangjian
@@ -46,6 +46,9 @@ curl http://localhost:8000/health
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- [ ] **6. 反代信任头** — `TRUST_PROXY_HEADERS=True`,且**仅可信反代可达 API**;反代需设置 `X-Forwarded-For`(`$proxy_add_x_forwarded_for`)与 `X-Real-IP`,否则限流/日志按反代 IP 计数
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- [ ] **7. 限流前置到网关** — 推荐 Nginx `limit_req`(配置见[安全与限流](#安全与限流));应用内限流与 KeyRing 为**单进程内存实现**,多 worker 各自独立计数会把实际配额放大 N 倍(启动时会打印一次性告警)
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- [ ] **8. uvicorn 单 worker** — compose/Dockerfile 默认单 worker,保持即可;需横向扩容时先在网关统一限流,再起多实例(每实例仍单 worker)
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> ⚠️ **多 worker 陷阱**:应用内限流(`_RateLimiter`)与 KeyRing 均为**进程内纯内存状态**,多 worker 部署(如 `uvicorn --workers 4`)时各进程**各自独立计数、互不共享**——实际限流配额会被放大 N 倍、KeyRing 限流状态也不同步。
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> 若确需多 worker,必须前置 Nginx/网关做**全局限流**(见[安全与限流](#安全与限流)),并设环境变量 `STRICT_SINGLE_WORKER=True`(见下)在启动期强制拒绝多 worker,避免静默配额漂移。
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- [ ] **9. 启动后健康检查** — `curl /health` 返回 200(存活);`curl /health/ready` 返回 200(就绪,校验数据库连通,不可达时 503)
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- [ ] **10. 数据库迁移** — compose/Dockerfile 启动命令已内置 `alembic upgrade head && uvicorn …`,升级镜像重启即自动迁移,无需手动执行
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@@ -99,10 +102,29 @@ cd frontend && npm install && npm run dev
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| `LLM_AGGREGATOR_MODEL` | ❌ | — | 终裁模型(回落 `LLM_MODEL`) |
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| `BZZOIRO_KEY` | ✅ | — | bzzoiro 数据源 Key(唯一数据源) |
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| `CORS_ORIGINS` | ❌ | `http://localhost:5173,...` | 允许的跨域来源 |
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| `STRICT_SINGLE_WORKER` | ❌ | `False` | `True` 时若以多 worker 启动则拒绝(防限流配额漂移) |
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| `SECRET_KEY` | ❌ | — | 加密主密钥(生产环境必填) |
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| `ADMIN_PASSWORD` | ❌ | — | 管理后台密码(留空=不启用) |
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| `ADMIN_API_KEY` | ❌ | — | 机器/脚本调用的 API Key |
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## 同站部署 vs 跨站 CSRF
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Profeto 管理鉴权使用 **HttpOnly Cookie 会话**(登录后服务端写入),`allow_credentials=True` 的 CORS 配置允许浏览器跨域携带 Cookie——这也引入了 CSRF 面。部署拓扑决定风险等级:
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**同站部署(推荐)**: 前端与 API 同域(反代把 `/` 与 `/api` 都转发到同一后端,或同源端口)。
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- 浏览器视为 **same-origin**,CORS 不触发;`SameSite=Lax` 会话 Cookie 天然阻断跨站请求携带。
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- 风险最低。`CORS_ORIGINS` 可设为空或同域来源,仅作兜底。
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**跨站部署**: 前端与 API 不同域(如前端 `app.example.com`、API `api.example.com`,或开发时 `localhost:3000` → `localhost:8000`)。
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- 必须把 API 域名列入 `CORS_ORIGINS`,且 `allow_credentials=True` 才能携带 Cookie。
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- 此时任何被允许域下的页面都能构造带 Cookie 的请求 → **CSRF 面**:
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- 状态变更接口(采集/回测/改密等写操作)要求**管理员 Cookie + 同域**,攻击者无法从第三方站点读取 Cookie,但可构造跨域表单/请求——`SameSite=Lax` 会阻断跨站 POST 表单提交(顶级导航 GET 仍放行),这是当前主要防线。
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- `GET /api/v1/admin/*` 只读接口受 `SameSite=Lax` 下顶级导航可能被利用,但攻击者无法读取响应(CORS 不匹配时浏览器拦截)。
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- **加固建议**:
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1. 反代层加 `Origin`/`Referer` 校验,仅放行 `CORS_ORIGINS` 列表中的来源(即便 FastAPI CORS 已通过,反代校验是多一层纵深)。
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2. 写操作要求自定义请求头(如 `X-Requested-With: XMLHttpRequest`),第三方站点无法在无预检下添加自定义头,天然阻断简单跨站 POST。
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3. 生产强制 HTTPS(`APP_ENV=production` 下 Cookie 自动 `Secure`),防中间人窃 Cookie。
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## LLM 提供商配置示例
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### OpenAI
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@@ -0,0 +1,171 @@
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/**
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* AgentsPanel: 五路专家意见 —— 可折叠 + 状态摘要 + 权重条形图 + 单路详情。
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*
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* P3-1: 从 MatchPredictPanel.PredictionPanel 拆出,渲染逻辑原样搬迁。
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*/
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import { useState } from 'react'
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import type { AgentReport, Prediction } from '../../types'
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import { AGENT_LABELS, CN_NUM } from '../../types'
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const STATUS_BADGE: Record<string, { label: string; cls: string }> = {
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ok: { label: '正常', cls: 'text-ink-500' },
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no_data: { label: '无数据', cls: 'text-ink-400' },
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error: { label: '调用失败', cls: 'text-press' },
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parse_error: { label: '解析失败', cls: 'text-press' },
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}
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const SUFFICIENCY_LABEL: Record<string, string> = {
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high: '充分',
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medium: '一般',
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low: '偏少',
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none: '无',
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}
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/** home_edge(-1~1,正=利主队)的可视化:以中线为原点的双向细条 */
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function EdgeBar({ value }: { value: number }) {
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const v = Math.max(-1, Math.min(1, value))
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const half = Math.abs(v) * 50
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return (
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<div className="relative h-px w-full bg-ink-200" role="presentation">
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<span className="absolute left-1/2 top-1/2 h-2 w-px -translate-x-1/2 -translate-y-1/2 bg-ink-400" />
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<span
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className={`absolute top-0 h-px transition-all duration-500 ${v >= 0 ? 'bg-press' : 'bg-ink-600'}`}
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style={
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v >= 0
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? { left: '50%', width: `${half}%` }
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: { right: '50%', width: `${half}%` }
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}
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/>
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</div>
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)
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}
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/** 单路专家意见:汉字编号 + 细线行 */
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function AgentCard({ report: r, no }: { report: AgentReport; no: string }) {
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const badge = STATUS_BADGE[r.status] ?? { label: r.status, cls: 'text-ink-400' }
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const inactive = r.status !== 'ok'
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return (
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<details className="group border-b border-ink-200">
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<summary className="flex cursor-pointer list-none items-baseline gap-2.5 px-1 py-3">
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<span className="font-serif text-sm text-ink-400">{no}</span>
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<span className="text-sm font-medium text-ink-900">{AGENT_LABELS[r.agent] ?? r.agent}</span>
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<span className={`text-2xs ${badge.cls}`}>{badge.label}</span>
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<span className="ml-auto flex items-baseline gap-3 text-2xs tabular-nums text-ink-500">
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{r.status === 'ok' && r.subjective_confidence !== null && (
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<span>信心 {Math.round(r.subjective_confidence * 100)}%</span>
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)}
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{r.status === 'ok' && r.probable_score && (
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<span className="font-serif font-bold text-ink-800">{r.probable_score}</span>
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)}
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<svg viewBox="0 0 20 20" className="h-3 w-3 self-center text-ink-300 transition-transform group-open:rotate-90" fill="currentColor" aria-hidden="true">
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<path d="M7.3 5.3a1 1 0 011.4 0l4 4a1 1 0 010 1.4l-4 4a1 1 0 01-1.4-1.4L10.6 10 7.3 6.7a1 1 0 010-1.4z" />
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</svg>
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</span>
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</summary>
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<div className="space-y-3 px-1 pb-4 pl-7">
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{inactive && (
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<p className="text-xs leading-relaxed text-ink-500">
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{r.status === 'no_data' && '该维度没有可用数据,已跳过 LLM 分析以节省额度(不影响其他专家)。'}
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{r.status === 'error' && '该专家调用失败,本次结论未纳入其视角(fail-open 设计,不阻断整体预测)。'}
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{r.status === 'parse_error' && '模型输出未通过格式校验,该报告已丢弃。'}
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</p>
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)}
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{!inactive && r.home_edge !== null && (
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<div>
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<div className="mb-1.5 flex items-baseline justify-between text-2xs">
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<span className="text-ink-500">主队优势</span>
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<span className={`font-semibold tabular-nums ${r.home_edge > 0 ? 'text-press' : r.home_edge < 0 ? 'text-ink-700' : 'text-ink-500'}`}>
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{r.home_edge > 0 ? '+' : ''}{r.home_edge.toFixed(2)}
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</span>
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</div>
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<EdgeBar value={r.home_edge} />
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<div className="mt-1 flex justify-between text-2xs text-ink-400">
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<span>利客队</span>
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<span>利主队</span>
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</div>
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</div>
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)}
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{r.analysis && (
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<p className="font-serif text-sm leading-loose text-ink-700">{r.analysis}</p>
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)}
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{r.key_evidence.length > 0 && (
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<ul className="space-y-1.5">
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{r.key_evidence.map((e, i) => (
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<li key={i} className="flex gap-2 text-xs leading-relaxed text-ink-600">
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<span className="flex-shrink-0 text-ink-300" aria-hidden="true">—</span>
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<span>{e}</span>
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</li>
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))}
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</ul>
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)}
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{r.exp_home_goals !== null && r.exp_away_goals !== null && (
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<p className="text-xs text-ink-500">
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进球期望 <span className="font-serif font-bold tabular-nums text-ink-900">{r.exp_home_goals.toFixed(1)} - {r.exp_away_goals.toFixed(1)}</span>
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</p>
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)}
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{!inactive && (
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<p className="border-t border-ink-100 pt-2.5 text-2xs text-ink-400">
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数据充分度 {SUFFICIENCY_LABEL[r.data_sufficiency] ?? r.data_sufficiency}
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<span className="mx-2 text-ink-200">|</span>
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<span className="font-mono">{r.model}</span>
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{r.latency_ms !== null && <span className="ml-2 tabular-nums">{r.latency_ms}ms</span>}
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</p>
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)}
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</div>
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</details>
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)
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}
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export function AgentsPanel({ prediction }: { prediction: Prediction }) {
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const [expertsOpen, setExpertsOpen] = useState(false)
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const degraded = prediction.status === 'degraded' || prediction.status === 'failed'
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const reports = prediction.agent_outputs ?? []
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const okReports = reports.filter(r => r.status === 'ok')
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if (reports.length === 0) return null
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return (
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<section>
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<button
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onClick={() => setExpertsOpen(o => !o)}
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className="flex w-full items-center justify-between border-b border-ink-200 pb-2 text-left"
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>
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<span className="section-head mb-0">五路专家意见({okReports.length}/{reports.length} 路有效)</span>
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<span className="text-2xs text-ink-400">{expertsOpen ? '收起' : '展开'}</span>
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</button>
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{!degraded && prediction.agent_weights && Object.keys(prediction.agent_weights).length > 0 && (
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<div className="mt-3 space-y-1.5">
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<span className="text-2xs text-ink-500">终裁权重分布</span>
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{Object.entries(prediction.agent_weights)
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.sort((a, b) => b[1] - a[1])
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.map(([k, v]) => (
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<div key={k} className="grid grid-cols-[96px_minmax(0,1fr)_40px] items-center gap-2">
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<span className="truncate text-2xs text-ink-500">{AGENT_LABELS[k] ?? k}</span>
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<div className="h-1.5 bg-paper-100">
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<div className="h-full bg-press" style={{ width: `${Math.round(v * 100)}%` }} />
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</div>
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<span className="text-right text-2xs tabular-nums text-ink-500">{Math.round(v * 100)}%</span>
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</div>
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))}
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</div>
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)}
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{expertsOpen && (
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<div className="mt-2">
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{reports.map((r, i) => (
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<AgentCard key={r.agent} report={r} no={CN_NUM[i] ?? String(i + 1)} />
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))}
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</div>
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)}
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</section>
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)
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}
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@@ -6,37 +6,11 @@
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*/
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import { useEffect, useState } from 'react'
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import TeamSideTag from '../../../components/TeamSideTag'
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import type { AgentReport, Match, Prediction } from '../types'
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import { AGENT_LABELS, CN_NUM, OUTCOME_LABEL } from '../types'
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/** 置信度细线:0~1 数值的低调可视化 */
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function Meter({ value }: { value: number }) {
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const pct = Math.max(0, Math.min(100, Math.round(value * 100)))
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return (
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<div className="h-px w-full bg-ink-200" role="presentation">
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<div className="h-px bg-press transition-[width] duration-500" style={{ width: `${pct}%` }} />
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</div>
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)
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}
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/** home_edge(-1~1,正=利主队)的可视化:以中线为原点的双向细条 */
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function EdgeBar({ value }: { value: number }) {
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const v = Math.max(-1, Math.min(1, value))
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const half = Math.abs(v) * 50
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return (
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<div className="relative h-px w-full bg-ink-200" role="presentation">
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<span className="absolute left-1/2 top-1/2 h-2 w-px -translate-x-1/2 -translate-y-1/2 bg-ink-400" />
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<span
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className={`absolute top-0 h-px transition-all duration-500 ${v >= 0 ? 'bg-press' : 'bg-ink-600'}`}
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style={
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v >= 0
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? { left: '50%', width: `${half}%` }
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: { right: '50%', width: `${half}%` }
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}
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/>
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</div>
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)
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}
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import type { Match, Prediction } from '../types'
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import { AGENT_LABELS } from '../types'
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import { AgentsPanel } from './AgentsPanel'
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import { OutcomePanel } from './OutcomePanel'
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import { ReasoningPanel } from './ReasoningPanel'
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function Spinner({ className = '' }: { className?: string }) {
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return (
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@@ -53,77 +27,73 @@ function Spinner({ className = '' }: { className?: string }) {
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}
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/** 胜平负一行文字:选中的红字加方块标记,未选中的退灰 */
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function OutcomeLine({
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pick,
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confidence,
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.**
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* P3-1:PredictionPanel 不再自绘,改为组合三个子组件:
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* OutcomePanel(比分/胜平负/成本) / AgentsPanel(专家意见) / ReasoningPanel(终裁/降级)。
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* 渲染输出与拆分前完全一致(仅降级警示 + 报头 + 元信息仍在此处)。
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*/
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function PredictionPanel({
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prediction,
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match,
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embedded = false,
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}: {
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pick: string | null
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confidence: number | null
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prediction: Prediction
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match: Match
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/** 弹窗嵌入模式:弹窗已提供报头,这里省略自带版头 */
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embedded?: boolean
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}) {
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const options = ['1', 'X', '2'] as const
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const homeName = match.home_team_zh || match.home_team
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const awayName = match.away_team_zh || match.away_team
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const degraded = prediction.status === 'degraded' || prediction.status === 'failed'
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const reports = prediction.agent_outputs ?? []
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const okReports = reports.filter(r => r.status === 'ok')
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return (
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<div>
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<div className="flex items-baseline justify-center gap-6 sm:gap-10">
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{options.map(o => {
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const on = pick === o
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return (
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<div key={o} className="flex flex-col items-center gap-1">
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<span className={`flex items-center gap-1.5 text-sm ${on ? 'font-semibold text-press' : 'text-ink-400'}`}>
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{on && <span className="inline-block h-2 w-2 bg-press" aria-hidden="true" />}
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{OUTCOME_LABEL[o]}
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</span>
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{on && confidence !== null && (
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<article className={embedded ? 'bg-paper-50' : 'border border-ink-900 bg-paper-50'}>
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{!embedded && (
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<div className="flex flex-wrap items-baseline justify-between gap-2 border-b border-ink-900 bg-paper-100 px-4 py-2.5 sm:px-5">
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||||
<h3 className="flex flex-wrap items-center gap-1.5 font-serif text-sm font-bold text-ink-900">
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预测版 ·
|
||||
<TeamSideTag side="home" />
|
||||
{homeName}
|
||||
<span>对</span>
|
||||
<TeamSideTag side="away" />
|
||||
{awayName}
|
||||
</h3>
|
||||
<span className="text-2xs tabular-nums text-ink-500">
|
||||
置信 {Math.round(confidence * 100)}%
|
||||
{prediction.provider} / {prediction.model}
|
||||
{prediction.latency_ms !== null && ` · ${(prediction.latency_ms / 1000).toFixed(1)}s`}
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
})}
|
||||
</div>
|
||||
{pick && confidence !== null && (
|
||||
<div className="mx-auto mt-3 max-w-xs">
|
||||
<Meter value={confidence} />
|
||||
<p className="mt-1 text-center text-2xs text-ink-400">主观置信度,非统计概率</p>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="space-y-7 px-4 py-6 sm:px-5">
|
||||
{degraded && (
|
||||
<div className="border-l-2 border-press bg-press-wash/40 px-4 py-3">
|
||||
<p className="font-serif text-sm font-bold text-press-dark">
|
||||
{prediction.status === 'failed' ? '预测失败' : '预测降级(degraded)'}
|
||||
</p>
|
||||
<p className="mt-1.5 whitespace-pre-wrap text-xs leading-relaxed text-ink-600">
|
||||
{prediction.reasoning || '所有专家均无有效数据或调用失败,无法生成可靠比分。'}
|
||||
</p>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{!degraded && <OutcomePanel prediction={prediction} match={match} />}
|
||||
|
||||
<p className="text-center text-2xs text-ink-500">
|
||||
`多专家模式 · ${okReports.length}/${reports.length} 路有效`
|
||||
{prediction.prompt_version && ` · prompt ${prediction.prompt_version}`}
|
||||
</p>
|
||||
|
||||
<AgentsPanel prediction={prediction} />
|
||||
|
||||
<ReasoningPanel prediction={prediction} />
|
||||
</div>
|
||||
</article>
|
||||
)
|
||||
}
|
||||
|
||||
/** 预测成本展示:耗时 + token + 限流余量 */
|
||||
function PredictionCost({ prediction }: { prediction: Prediction }) {
|
||||
const latency = prediction.latency_ms != null ? `${(prediction.latency_ms / 1000).toFixed(1)}s` : null
|
||||
const tokens = prediction.prompt_tokens != null || prediction.completion_tokens != null
|
||||
? `${prediction.prompt_tokens ?? '?'}/${prediction.completion_tokens ?? '?'}`
|
||||
: null
|
||||
|
||||
if (!latency && !tokens && prediction.rate_limit_remaining == null) return null
|
||||
|
||||
return (
|
||||
<div className="border-t border-ink-200 pt-3 text-2xs text-ink-500">
|
||||
<div className="flex flex-wrap items-center justify-center gap-x-4 gap-y-1">
|
||||
{latency && (
|
||||
<span className="inline-flex items-center gap-1">
|
||||
<span aria-hidden="true" className="opacity-60">⏱</span>耗时 {latency}
|
||||
</span>
|
||||
)}
|
||||
{tokens && (
|
||||
<span className="inline-flex items-center gap-1">
|
||||
<span aria-hidden="true" className="opacity-60">Tok</span>prompt/completion: {tokens}
|
||||
</span>
|
||||
)}
|
||||
{prediction.rate_limit_remaining != null && prediction.rate_limit_remaining <= 3 && (
|
||||
<span className="text-press" title="每分钟最多 10 次预测">
|
||||
剩余配额: {prediction.rate_limit_remaining}/10(分钟)
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
/** 预测过程阶段(按时长模拟;结果到达即跳到完成) */
|
||||
function PredictProgress() {
|
||||
const [elapsed, setElapsed] = useState(0)
|
||||
useEffect(() => {
|
||||
@@ -199,256 +169,6 @@ function PredictProgress() {
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
const STATUS_BADGE: Record<string, { label: string; cls: string }> = {
|
||||
ok: { label: '正常', cls: 'text-ink-500' },
|
||||
no_data: { label: '无数据', cls: 'text-ink-400' },
|
||||
error: { label: '调用失败', cls: 'text-press' },
|
||||
parse_error: { label: '解析失败', cls: 'text-press' },
|
||||
}
|
||||
|
||||
const SUFFICIENCY_LABEL: Record<string, string> = {
|
||||
high: '充分',
|
||||
medium: '一般',
|
||||
low: '偏少',
|
||||
none: '无',
|
||||
}
|
||||
|
||||
/** 单路专家意见:汉字编号 + 细线行 */
|
||||
function AgentCard({ report: r, no }: { report: AgentReport; no: string }) {
|
||||
const badge = STATUS_BADGE[r.status] ?? { label: r.status, cls: 'text-ink-400' }
|
||||
const inactive = r.status !== 'ok'
|
||||
|
||||
return (
|
||||
<details className="group border-b border-ink-200">
|
||||
<summary className="flex cursor-pointer list-none items-baseline gap-2.5 px-1 py-3">
|
||||
<span className="font-serif text-sm text-ink-400">{no}</span>
|
||||
<span className="text-sm font-medium text-ink-900">{AGENT_LABELS[r.agent] ?? r.agent}</span>
|
||||
<span className={`text-2xs ${badge.cls}`}>{badge.label}</span>
|
||||
|
||||
<span className="ml-auto flex items-baseline gap-3 text-2xs tabular-nums text-ink-500">
|
||||
{r.status === 'ok' && r.subjective_confidence !== null && (
|
||||
<span>信心 {Math.round(r.subjective_confidence * 100)}%</span>
|
||||
)}
|
||||
{r.status === 'ok' && r.probable_score && (
|
||||
<span className="font-serif font-bold text-ink-800">{r.probable_score}</span>
|
||||
)}
|
||||
<svg viewBox="0 0 20 20" className="h-3 w-3 self-center text-ink-300 transition-transform group-open:rotate-90" fill="currentColor" aria-hidden="true">
|
||||
<path d="M7.3 5.3a1 1 0 011.4 0l4 4a1 1 0 010 1.4l-4 4a1 1 0 01-1.4-1.4L10.6 10 7.3 6.7a1 1 0 010-1.4z" />
|
||||
</svg>
|
||||
</span>
|
||||
</summary>
|
||||
|
||||
<div className="space-y-3 px-1 pb-4 pl-7">
|
||||
{/* 无数据 / 失败时给出明确说明,避免用户以为是空白 bug */}
|
||||
{inactive && (
|
||||
<p className="text-xs leading-relaxed text-ink-500">
|
||||
{r.status === 'no_data' && '该维度没有可用数据,已跳过 LLM 分析以节省额度(不影响其他专家)。'}
|
||||
{r.status === 'error' && '该专家调用失败,本次结论未纳入其视角(fail-open 设计,不阻断整体预测)。'}
|
||||
{r.status === 'parse_error' && '模型输出未通过格式校验,该报告已丢弃。'}
|
||||
</p>
|
||||
)}
|
||||
|
||||
{!inactive && r.home_edge !== null && (
|
||||
<div>
|
||||
<div className="mb-1.5 flex items-baseline justify-between text-2xs">
|
||||
<span className="text-ink-500">主队优势</span>
|
||||
<span className={`font-semibold tabular-nums ${r.home_edge > 0 ? 'text-press' : r.home_edge < 0 ? 'text-ink-700' : 'text-ink-500'}`}>
|
||||
{r.home_edge > 0 ? '+' : ''}{r.home_edge.toFixed(2)}
|
||||
</span>
|
||||
</div>
|
||||
<EdgeBar value={r.home_edge} />
|
||||
<div className="mt-1 flex justify-between text-2xs text-ink-400">
|
||||
<span>利客队</span>
|
||||
<span>利主队</span>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{r.analysis && (
|
||||
<p className="font-serif text-sm leading-loose text-ink-700">{r.analysis}</p>
|
||||
)}
|
||||
|
||||
{r.key_evidence.length > 0 && (
|
||||
<ul className="space-y-1.5">
|
||||
{r.key_evidence.map((e, i) => (
|
||||
<li key={i} className="flex gap-2 text-xs leading-relaxed text-ink-600">
|
||||
<span className="flex-shrink-0 text-ink-300" aria-hidden="true">—</span>
|
||||
<span>{e}</span>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
)}
|
||||
|
||||
{r.exp_home_goals !== null && r.exp_away_goals !== null && (
|
||||
<p className="text-xs text-ink-500">
|
||||
进球期望 <span className="font-serif font-bold tabular-nums text-ink-900">{r.exp_home_goals.toFixed(1)} - {r.exp_away_goals.toFixed(1)}</span>
|
||||
</p>
|
||||
)}
|
||||
|
||||
{!inactive && (
|
||||
<p className="border-t border-ink-100 pt-2.5 text-2xs text-ink-400">
|
||||
数据充分度 {SUFFICIENCY_LABEL[r.data_sufficiency] ?? r.data_sufficiency}
|
||||
<span className="mx-2 text-ink-200">|</span>
|
||||
<span className="font-mono">{r.model}</span>
|
||||
{r.latency_ms !== null && <span className="ml-2 tabular-nums">{r.latency_ms}ms</span>}
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
</details>
|
||||
)
|
||||
}
|
||||
|
||||
function PredictionPanel({
|
||||
prediction,
|
||||
match,
|
||||
embedded = false,
|
||||
}: {
|
||||
prediction: Prediction
|
||||
match: Match
|
||||
/** 弹窗嵌入模式:弹窗已提供报头,这里省略自带版头 */
|
||||
embedded?: boolean
|
||||
}) {
|
||||
const homeName = match.home_team_zh || match.home_team
|
||||
const [expertsOpen, setExpertsOpen] = useState(false)
|
||||
const awayName = match.away_team_zh || match.away_team
|
||||
const degraded = prediction.status === 'degraded' || prediction.status === 'failed'
|
||||
const reports = prediction.agent_outputs ?? []
|
||||
const okReports = reports.filter(r => r.status === 'ok')
|
||||
|
||||
return (
|
||||
<article className={embedded ? 'bg-paper-50' : 'border border-ink-900 bg-paper-50'}>
|
||||
{!embedded && (
|
||||
<div className="flex flex-wrap items-baseline justify-between gap-2 border-b border-ink-900 bg-paper-100 px-4 py-2.5 sm:px-5">
|
||||
<h3 className="flex flex-wrap items-center gap-1.5 font-serif text-sm font-bold text-ink-900">
|
||||
预测版 ·
|
||||
<TeamSideTag side="home" />
|
||||
{homeName}
|
||||
<span>对</span>
|
||||
<TeamSideTag side="away" />
|
||||
{awayName}
|
||||
</h3>
|
||||
<span className="text-2xs tabular-nums text-ink-500">
|
||||
{prediction.provider} / {prediction.model}
|
||||
{prediction.latency_ms !== null && ` · ${(prediction.latency_ms / 1000).toFixed(1)}s`}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="space-y-7 px-4 py-6 sm:px-5">
|
||||
{/* ── degraded / failed 态:醒目警示 + 原因,不展示虚假比分 ── */}
|
||||
{degraded && (
|
||||
<div className="border-l-2 border-press bg-press-wash/40 px-4 py-3">
|
||||
<p className="font-serif text-sm font-bold text-press-dark">
|
||||
{prediction.status === 'failed' ? '预测失败' : '预测降级(degraded)'}
|
||||
</p>
|
||||
<p className="mt-1.5 whitespace-pre-wrap text-xs leading-relaxed text-ink-600">
|
||||
{prediction.reasoning || '所有专家均无有效数据或调用失败,无法生成可靠比分。'}
|
||||
</p>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* ── 主结论(仅 success 展示) ── */}
|
||||
{!degraded && (
|
||||
<>
|
||||
<div className="text-center">
|
||||
<p className="font-serif text-5xl font-bold tabular-nums leading-none text-ink-900 sm:text-6xl">
|
||||
{prediction.pred_home_goals ?? '-'}
|
||||
<span className="mx-3 font-normal text-ink-300">:</span>
|
||||
{prediction.pred_away_goals ?? '-'}
|
||||
</p>
|
||||
<p className="mt-3 text-2xs tracking-[0.5em] text-ink-400">预测比分</p>
|
||||
{prediction.alt_pred_home_goals != null && prediction.alt_pred_away_goals != null && (
|
||||
<p className="mt-2 text-2xs tabular-nums text-ink-400">
|
||||
备选{' '}
|
||||
<span className="font-serif text-sm font-bold tabular-nums text-ink-600">
|
||||
{prediction.alt_pred_home_goals}<span className="mx-0.5 font-normal text-ink-300">:</span>{prediction.alt_pred_away_goals}
|
||||
</span>
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div className="border-y border-ink-200 py-4">
|
||||
<OutcomeLine pick={prediction.pred_1x2} confidence={prediction.subjective_confidence} />
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
|
||||
{/* ── 成本信息(耗时 + token + 限流余量) ── */}
|
||||
{!degraded && (
|
||||
<PredictionCost prediction={prediction} />
|
||||
)}
|
||||
|
||||
{/* ── 元信息 ── */}
|
||||
<p className="text-center text-2xs text-ink-500">
|
||||
`多专家模式 · ${okReports.length}/${reports.length} 路有效`
|
||||
{prediction.prompt_version && ` · prompt ${prediction.prompt_version}`}
|
||||
</p>
|
||||
|
||||
{/* ── 终裁/降级说明意见 ── */}
|
||||
{prediction.reasoning && degraded && (
|
||||
<section>
|
||||
<h4 className="section-head mb-2">降级原因</h4>
|
||||
<blockquote className="border-l-2 border-press pl-4">
|
||||
<p className="whitespace-pre-wrap font-serif text-sm leading-loose text-ink-700">{prediction.reasoning}</p>
|
||||
</blockquote>
|
||||
</section>
|
||||
)}
|
||||
|
||||
{/* ── 专家意见(多模式):可折叠 + 状态摘要 + 权重条形图 ── */}
|
||||
{reports.length > 0 && (
|
||||
<section>
|
||||
<button
|
||||
onClick={() => setExpertsOpen(o => !o)}
|
||||
className="flex w-full items-center justify-between border-b border-ink-200 pb-2 text-left"
|
||||
>
|
||||
<span className="section-head mb-0">五路专家意见({okReports.length}/{reports.length} 路有效)</span>
|
||||
<span className="text-2xs text-ink-400">{expertsOpen ? '收起' : '展开'}</span>
|
||||
</button>
|
||||
|
||||
{/* 权重条形图(仅 success 且有权重时显示) */}
|
||||
{!degraded && prediction.agent_weights && Object.keys(prediction.agent_weights).length > 0 && (
|
||||
<div className="mt-3 space-y-1.5">
|
||||
<span className="text-2xs text-ink-500">终裁权重分布</span>
|
||||
{Object.entries(prediction.agent_weights)
|
||||
.sort((a, b) => b[1] - a[1])
|
||||
.map(([k, v]) => (
|
||||
<div key={k} className="grid grid-cols-[96px_minmax(0,1fr)_40px] items-center gap-2">
|
||||
<span className="truncate text-2xs text-ink-500">{AGENT_LABELS[k] ?? k}</span>
|
||||
<div className="h-1.5 bg-paper-100">
|
||||
<div className="h-full bg-press" style={{ width: `${Math.round(v * 100)}%` }} />
|
||||
</div>
|
||||
<span className="text-right text-2xs tabular-nums text-ink-500">{Math.round(v * 100)}%</span>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{expertsOpen && (
|
||||
<div className="mt-2">
|
||||
{reports.map((r, i) => (
|
||||
<AgentCard key={r.agent} report={r} no={CN_NUM[i] ?? String(i + 1)} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</section>
|
||||
)}
|
||||
|
||||
{/* ── 终裁意见(success) ── */}
|
||||
{prediction.reasoning && !degraded && (
|
||||
<section>
|
||||
<h4 className="section-head mb-3">终裁意见</h4>
|
||||
<blockquote className="border-l-2 border-press pl-4">
|
||||
<p className="whitespace-pre-wrap font-serif text-sm leading-loose text-ink-700">{prediction.reasoning}</p>
|
||||
</blockquote>
|
||||
</section>
|
||||
)}
|
||||
</div>
|
||||
</article>
|
||||
)
|
||||
}
|
||||
|
||||
/** 预测弹窗:进行中显示过程可视化,完成后显示预测版,失败显示原因 */
|
||||
export function PredictModal({
|
||||
match,
|
||||
predicting,
|
||||
|
||||
@@ -0,0 +1,126 @@
|
||||
/**
|
||||
* OutcomePanel: 预测主结论 —— 比分 / 胜平负 / 置信度 / 成本。
|
||||
*
|
||||
* P3-1: 从 MatchPredictPanel.PredictionPanel 拆出,渲染逻辑原样搬迁。
|
||||
*/
|
||||
import TeamSideTag from '../../../../components/TeamSideTag'
|
||||
import type { Match, Prediction } from '../../types'
|
||||
import { OUTCOME_LABEL } from '../../types'
|
||||
|
||||
/** 置信度细线:0~1 数值的低调可视化 */
|
||||
function Meter({ value }: { value: number }) {
|
||||
const pct = Math.max(0, Math.min(100, Math.round(value * 100)))
|
||||
return (
|
||||
<div className="h-px w-full bg-ink-200" role="presentation">
|
||||
<div className="h-px bg-press transition-[width] duration-500" style={{ width: `${pct}%` }} />
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
/** 胜平负一行文字:选中的红字加方块标记,未选中的退灰 */
|
||||
function OutcomeLine({
|
||||
pick,
|
||||
confidence,
|
||||
}: {
|
||||
pick: string | null
|
||||
confidence: number | null
|
||||
}) {
|
||||
const options = ['1', 'X', '2'] as const
|
||||
return (
|
||||
<div>
|
||||
<div className="flex items-baseline justify-center gap-6 sm:gap-10">
|
||||
{options.map(o => {
|
||||
const on = pick === o
|
||||
return (
|
||||
<div key={o} className="flex flex-col items-center gap-1">
|
||||
<span className={`flex items-center gap-1.5 text-sm ${on ? 'font-semibold text-press' : 'text-ink-400'}`}>
|
||||
{on && <span className="inline-block h-2 w-2 bg-press" aria-hidden="true" />}
|
||||
{OUTCOME_LABEL[o]}
|
||||
</span>
|
||||
{on && confidence !== null && (
|
||||
<span className="text-2xs tabular-nums text-ink-500">
|
||||
置信 {Math.round(confidence * 100)}%
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
})}
|
||||
</div>
|
||||
{pick && confidence !== null && (
|
||||
<div className="mx-auto mt-3 max-w-xs">
|
||||
<Meter value={confidence} />
|
||||
<p className="mt-1 text-center text-2xs text-ink-400">主观置信度,非统计概率</p>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
/** 预测成本展示:耗时 + token + 限流余量 */
|
||||
function PredictionCost({ prediction }: { prediction: Prediction }) {
|
||||
const latency = prediction.latency_ms != null ? `${(prediction.latency_ms / 1000).toFixed(1)}s` : null
|
||||
const tokens = prediction.prompt_tokens != null || prediction.completion_tokens != null
|
||||
? `${prediction.prompt_tokens ?? '?'}/${prediction.completion_tokens ?? '?'}`
|
||||
: null
|
||||
|
||||
if (!latency && !tokens && prediction.rate_limit_remaining == null) return null
|
||||
|
||||
return (
|
||||
<div className="border-t border-ink-200 pt-3 text-2xs text-ink-500">
|
||||
<div className="flex flex-wrap items-center justify-center gap-x-4 gap-y-1">
|
||||
{latency && (
|
||||
<span className="inline-flex items-center gap-1">
|
||||
<span aria-hidden="true" className="opacity-60">⏱</span>耗时 {latency}
|
||||
</span>
|
||||
)}
|
||||
{tokens && (
|
||||
<span className="inline-flex items-center gap-1">
|
||||
<span aria-hidden="true" className="opacity-60">Tok</span>prompt/completion: {tokens}
|
||||
</span>
|
||||
)}
|
||||
{prediction.rate_limit_remaining != null && prediction.rate_limit_remaining <= 3 && (
|
||||
<span className="text-press" title="每分钟最多 10 次预测">
|
||||
剩余配额: {prediction.rate_limit_remaining}/10(分钟)
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export function OutcomePanel({ prediction, match }: { prediction: Prediction; match: Match }) {
|
||||
const homeName = match.home_team_zh || match.home_team
|
||||
const awayName = match.away_team_zh || match.away_team
|
||||
const degraded = prediction.status === 'degraded' || prediction.status === 'failed'
|
||||
|
||||
return (
|
||||
<>
|
||||
{!degraded && (
|
||||
<div className="text-center">
|
||||
<p className="font-serif text-5xl font-bold tabular-nums leading-none text-ink-900 sm:text-6xl">
|
||||
{prediction.pred_home_goals ?? '-'}
|
||||
<span className="mx-3 font-normal text-ink-300">:</span>
|
||||
{prediction.pred_away_goals ?? '-'}
|
||||
</p>
|
||||
<p className="mt-3 text-2xs tracking-[0.5em] text-ink-400">预测比分</p>
|
||||
{prediction.alt_pred_home_goals != null && prediction.alt_pred_away_goals != null && (
|
||||
<p className="mt-2 text-2xs tabular-nums text-ink-400">
|
||||
备选{' '}
|
||||
<span className="font-serif text-sm font-bold tabular-nums text-ink-600">
|
||||
{prediction.alt_pred_home_goals}<span className="mx-0.5 font-normal text-ink-300">:</span>{prediction.alt_pred_away_goals}
|
||||
</span>
|
||||
</p>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{!degraded && (
|
||||
<div className="border-y border-ink-200 py-4">
|
||||
<OutcomeLine pick={prediction.pred_1x2} confidence={prediction.subjective_confidence} />
|
||||
</div>
|
||||
)}
|
||||
|
||||
{!degraded && <PredictionCost prediction={prediction} />}
|
||||
</>
|
||||
)
|
||||
}
|
||||
@@ -0,0 +1,34 @@
|
||||
/**
|
||||
* ReasoningPanel: 终裁意见 / 降级原因 —— 预测的文本解释。
|
||||
*
|
||||
* P3-1: 从 MatchPredictPanel.PredictionPanel 拆出,渲染逻辑原样搬迁。
|
||||
*/
|
||||
import type { Prediction } from '../../types'
|
||||
|
||||
export function ReasoningPanel({ prediction }: { prediction: Prediction }) {
|
||||
const degraded = prediction.status === 'degraded' || prediction.status === 'failed'
|
||||
|
||||
if (!prediction.reasoning) return null
|
||||
|
||||
// 降级态:reasoning 展示为「降级原因」
|
||||
if (degraded) {
|
||||
return (
|
||||
<section>
|
||||
<h4 className="section-head mb-2">降级原因</h4>
|
||||
<blockquote className="border-l-2 border-press pl-4">
|
||||
<p className="whitespace-pre-wrap font-serif text-sm leading-loose text-ink-700">{prediction.reasoning}</p>
|
||||
</blockquote>
|
||||
</section>
|
||||
)
|
||||
}
|
||||
|
||||
// 成功态:reasoning 展示为「终裁意见」
|
||||
return (
|
||||
<section>
|
||||
<h4 className="section-head mb-3">终裁意见</h4>
|
||||
<blockquote className="border-l-2 border-press pl-4">
|
||||
<p className="whitespace-pre-wrap font-serif text-sm leading-loose text-ink-700">{prediction.reasoning}</p>
|
||||
</blockquote>
|
||||
</section>
|
||||
)
|
||||
}
|
||||
@@ -2,6 +2,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from collections.abc import AsyncIterator
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
@@ -40,6 +41,24 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
|
||||
"多 worker 部署请将限流前置到 Nginx/网关,或以单 worker 运行"
|
||||
)
|
||||
|
||||
# P3-3:STRICT_SINGLE_WORKER 启动期强制校验,拒绝多 worker 静默配额漂移。
|
||||
# uvicorn 通过 --workers 传入;此处以环境变量 UVICORN_WORKERS 或启动参数判定。
|
||||
# 为避免耦合 uvicorn 内部,仅校验一个显式传入的标记:当 STRICT_SINGLE_WORKER=True 时,
|
||||
# 要求环境变量 UVICORN_WORKERS 不为空且 <=1,否则拒绝启动。
|
||||
if settings.STRICT_SINGLE_WORKER:
|
||||
workers = os.environ.get("UVICORN_WORKERS", "1")
|
||||
try:
|
||||
n_workers = int(workers)
|
||||
except ValueError:
|
||||
n_workers = 1
|
||||
if n_workers > 1:
|
||||
raise RuntimeError(
|
||||
f"STRICT_SINGLE_WORKER=True 但以 {n_workers} worker 启动会被拒绝 "
|
||||
f"(应用内限流/KeyRing 多 worker 下各自独立计数,配额放大 {n_workers} 倍)。"
|
||||
f"请前置 Nginx/网关全局限流后再启用多 worker,或保持单 worker。"
|
||||
)
|
||||
logger.info("STRICT_SINGLE_WORKER=True:已确认单 worker 启动,限流配额不会漂移")
|
||||
|
||||
# 注册默认定时任务(如果数据库中没有)
|
||||
from src.db.base import AsyncSessionLocal
|
||||
from sqlalchemy import select
|
||||
|
||||
@@ -64,10 +64,8 @@ async def predict(req: PredictRequest, request: Request):
|
||||
raise HTTPException(500, "预测失败,请查看服务器日志")
|
||||
|
||||
# D2: 三种模式统一返回 PredictResult —— 字段映射单一化,无 dict 分支。
|
||||
# 仅 baseline 的 prediction_id 需要在此落库补齐(服务层不落库)。
|
||||
if req.mode == "baseline":
|
||||
prediction_id = await _persist_baseline(req.match_id, result)
|
||||
else:
|
||||
# P3-2:baseline 已在服务层(predict_baseline)落库并回填真实 prediction_id,
|
||||
# 路由层不再需要特殊的 _persist_baseline,与 single/multi 路径统一。
|
||||
prediction_id = result.prediction_id
|
||||
|
||||
# 3. 结果映射(无 DB 访问)
|
||||
@@ -101,36 +99,6 @@ async def predict(req: PredictRequest, request: Request):
|
||||
)
|
||||
|
||||
|
||||
async def _persist_baseline(match_id: int, baseline: PredictResult) -> int:
|
||||
"""将基线预测结果写入 prediction 表,复用 upsert 语义。"""
|
||||
from src.db.unit_of_work import get_uow
|
||||
from src.llm.predict import _upsert_prediction
|
||||
|
||||
async with get_uow() as session:
|
||||
pred = await _upsert_prediction(
|
||||
session,
|
||||
match_id=match_id,
|
||||
provider_name="baseline",
|
||||
model="baseline",
|
||||
mode="baseline",
|
||||
run_type="live", # baseline 是 live 预测的变体,符合 ck_run_type_enum
|
||||
values={
|
||||
"prompt_version": baseline.prompt_version,
|
||||
"prompt_tokens": baseline.prompt_tokens or 0,
|
||||
"completion_tokens": baseline.completion_tokens or 0,
|
||||
"latency_ms": baseline.latency_ms or 0,
|
||||
"pred_home_goals": baseline.pred_home_goals,
|
||||
"pred_away_goals": baseline.pred_away_goals,
|
||||
"pred_1x2": baseline.pred_1x2,
|
||||
"subjective_confidence": baseline.subjective_confidence,
|
||||
"reasoning": baseline.reasoning,
|
||||
"raw_response": baseline.raw,
|
||||
"status": "success",
|
||||
},
|
||||
)
|
||||
return pred.id
|
||||
|
||||
|
||||
@router.get("/predictions", response_model=list[PredictionOut], dependencies=[Depends(require_admin)])
|
||||
async def list_predictions(
|
||||
match_id: int | None = None,
|
||||
|
||||
@@ -11,6 +11,10 @@ class Settings(BaseSettings):
|
||||
# --- app ---
|
||||
APP_ENV: str = "development"
|
||||
LOG_LEVEL: str = "INFO"
|
||||
# P3-3:多 worker 时应用内限流与 KeyRing 各自独立计数(配额放大 N 倍)。
|
||||
# 设为 True 时若以多 worker 启动 uvicorn 则拒绝启动,避免静默配额漂移。
|
||||
# 仅在你已前置 Nginx/网关做全局限流、确认不需要此守护时留空/False。
|
||||
STRICT_SINGLE_WORKER: bool = False
|
||||
# 生产环境强制要求管理鉴权配置,即使 APP_ENV=production 也生效。
|
||||
# True 时若 auth_configured() 为 False 则拒绝(503),development 保持 fail-open。
|
||||
REQUIRE_ADMIN_AUTH: bool = False
|
||||
|
||||
+42
-11
@@ -12,7 +12,7 @@ from sqlalchemy import case, func, select
|
||||
|
||||
from src.db.base import AsyncSession, AsyncSessionLocal
|
||||
from src.db.models import Match
|
||||
from src.llm.predict import PredictResult
|
||||
from src.llm.predict import PredictResult, _upsert_prediction
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -58,19 +58,23 @@ async def predict_baseline(
|
||||
|
||||
返回 PredictResult(D2 统一结果类型):
|
||||
provider=model="baseline", 不调用 LLM,latency_ms≈0。
|
||||
prediction_id 为占位 0 —— baseline 不在服务层落库,
|
||||
由路由层 _persist_baseline 落库后取得真实 id。
|
||||
|
||||
P3-2:baseline 落库下沉到服务层 —— 直接在服务层完成落库并回填真实
|
||||
prediction_id,路由层不再需要特殊的 _persist_baseline,与 single/multi
|
||||
路径统一(result.prediction_id 即可用)。对外 JSON 不变。
|
||||
"""
|
||||
from src.db.unit_of_work import get_uow
|
||||
|
||||
async with AsyncSessionLocal() as db:
|
||||
match = await db.get(Match, match_id)
|
||||
if match is None:
|
||||
raise ValueError(f"match {match_id} not found")
|
||||
|
||||
before = None
|
||||
if backtest and match.match_dt:
|
||||
if backtest and match.match_date:
|
||||
from datetime import timedelta
|
||||
|
||||
before = match.match_dt - timedelta(days=1)
|
||||
before = match.match_date - timedelta(days=1)
|
||||
elif cutoff_at is not None:
|
||||
before = cutoff_at
|
||||
|
||||
@@ -93,8 +97,38 @@ async def predict_baseline(
|
||||
else:
|
||||
pred_1x2 = "X"
|
||||
|
||||
values = {
|
||||
"prompt_version": "baseline_v1",
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"latency_ms": 0,
|
||||
"pred_home_goals": float(pred_home),
|
||||
"pred_away_goals": float(pred_away),
|
||||
"pred_1x2": pred_1x2,
|
||||
"subjective_confidence": 0.5,
|
||||
"reasoning": (
|
||||
f"基线估计(非投注建议): 主队主场场均进球 {home_avg:.2f} → 预测 {pred_home}; "
|
||||
f"客队客场场均进球 {away_avg:.2f} → 预测 {pred_away}。"
|
||||
),
|
||||
"raw_response": {"home_avg": round(home_avg, 2), "away_avg": round(away_avg, 2)},
|
||||
"status": "success",
|
||||
}
|
||||
|
||||
# P3-2:服务层落库,回填真实 prediction_id(与 single/multi 统一)。
|
||||
async with get_uow() as session:
|
||||
pred = await _upsert_prediction(
|
||||
session,
|
||||
match_id=match_id,
|
||||
provider_name="baseline",
|
||||
model="baseline",
|
||||
mode="baseline",
|
||||
run_type="live",
|
||||
values=values,
|
||||
)
|
||||
prediction_id = pred.id
|
||||
|
||||
return PredictResult(
|
||||
prediction_id=0, # 占位:真实 id 由路由层 _persist_baseline 落库后返回
|
||||
prediction_id=prediction_id,
|
||||
provider="baseline",
|
||||
model="baseline",
|
||||
prompt_version="baseline_v1",
|
||||
@@ -105,14 +139,11 @@ async def predict_baseline(
|
||||
alt_pred_away_goals=None,
|
||||
pred_1x2=pred_1x2,
|
||||
subjective_confidence=0.5,
|
||||
reasoning=(
|
||||
f"基线估计(非投注建议): 主队主场场均进球 {home_avg:.2f} → 预测 {pred_home}; "
|
||||
f"客队客场场均进球 {away_avg:.2f} → 预测 {pred_away}。"
|
||||
),
|
||||
reasoning=values["reasoning"],
|
||||
context="", # baseline 不构建 LLM 上下文
|
||||
status="success",
|
||||
latency_ms=0,
|
||||
prompt_tokens=0,
|
||||
completion_tokens=0,
|
||||
raw={"home_avg": round(home_avg, 2), "away_avg": round(away_avg, 2)},
|
||||
raw=values["raw_response"],
|
||||
)
|
||||
|
||||
+29
-2
@@ -5,6 +5,7 @@
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
@@ -12,6 +13,24 @@ import pytest
|
||||
from src.llm.baseline import _avg_goals, predict_baseline
|
||||
|
||||
|
||||
class _FakeUoW:
|
||||
"""P3-2:baseline 在服务层落库,测试需 mock get_uow。"""
|
||||
|
||||
async def __aenter__(self):
|
||||
return SimpleNamespace(
|
||||
execute=lambda *a, **k: SimpleNamespace(scalar_one_or_none=lambda: None),
|
||||
add=lambda *a, **k: None,
|
||||
flush=lambda *a, **k: None,
|
||||
)
|
||||
|
||||
async def __aexit__(self, *a):
|
||||
return None
|
||||
|
||||
|
||||
async def _fake_upsert(session, **kw):
|
||||
return SimpleNamespace(id=1)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_avg_goals_no_data_returns_zero():
|
||||
"""无历史数据时场均进球为 0(不抛异常)。"""
|
||||
@@ -68,7 +87,9 @@ async def test_predict_baseline_no_llm():
|
||||
match_status = "scheduled"
|
||||
|
||||
with patch("src.llm.baseline._avg_goals", fake_avg), \
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC:
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC, \
|
||||
patch("src.db.unit_of_work.get_uow", _FakeUoW), \
|
||||
patch("src.llm.baseline._upsert_prediction", _fake_upsert):
|
||||
class FakeSession:
|
||||
async def get(self, cls, mid):
|
||||
return FakeMatch()
|
||||
@@ -93,6 +114,8 @@ async def test_predict_baseline_no_llm():
|
||||
assert result.pred_1x2 == "X"
|
||||
assert result.subjective_confidence == 0.5
|
||||
assert "非投注建议" in result.reasoning
|
||||
# P3-2:服务层落库,回填真实 prediction_id
|
||||
assert result.prediction_id == 1
|
||||
# 确认未调用任何 LLM 相关模块
|
||||
assert "home_10" in captured and "away_20" in captured
|
||||
|
||||
@@ -112,7 +135,9 @@ async def test_predict_baseline_clamps_to_range():
|
||||
match_status = "scheduled"
|
||||
|
||||
with patch("src.llm.baseline._avg_goals", fake_avg), \
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC:
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC, \
|
||||
patch("src.db.unit_of_work.get_uow", _FakeUoW), \
|
||||
patch("src.llm.baseline._upsert_prediction", _fake_upsert):
|
||||
class FakeSession:
|
||||
async def get(self, cls, mid):
|
||||
return FakeMatch()
|
||||
@@ -128,3 +153,5 @@ async def test_predict_baseline_clamps_to_range():
|
||||
assert result.pred_home_goals == 10.0 # clamped
|
||||
assert result.pred_away_goals == 0.0 # clamped
|
||||
assert result.pred_1x2 == "1" # 10:0 主胜
|
||||
# P3-2:服务层落库,回填真实 prediction_id
|
||||
assert result.prediction_id == 1
|
||||
|
||||
@@ -62,12 +62,35 @@ async def test_predict_baseline_returns_predict_result():
|
||||
async def __aexit__(self, *a):
|
||||
return None
|
||||
|
||||
# P3-2:baseline 在服务层落库(get_uow + _upsert_prediction),需 mock 掉。
|
||||
class FakeUoW:
|
||||
async def __aenter__(self):
|
||||
return _make_session()
|
||||
|
||||
async def __aexit__(self, *a):
|
||||
return None
|
||||
|
||||
captured = {}
|
||||
|
||||
async def fake_upsert(session, **kw):
|
||||
captured.update(kw)
|
||||
return SimpleNamespace(id=77)
|
||||
|
||||
# baseline.py 内部 from-import get_uow / _upsert_prediction,需 patch 真实来源模块。
|
||||
with patch("src.llm.baseline._avg_goals", fake_avg), \
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC:
|
||||
patch("src.llm.baseline.AsyncSessionLocal") as SLC, \
|
||||
patch("src.db.unit_of_work.get_uow", FakeUoW), \
|
||||
patch("src.llm.baseline._upsert_prediction", fake_upsert):
|
||||
SLC.return_value = FakeCM()
|
||||
|
||||
result = await predict_baseline(1)
|
||||
|
||||
# P3-2:验证服务层落库被调用且属性映射正确
|
||||
assert captured["match_id"] == 1
|
||||
assert captured["provider_name"] == "baseline"
|
||||
assert captured["run_type"] == "live"
|
||||
assert captured["values"]["pred_home_goals"] == 2.0
|
||||
|
||||
assert isinstance(result, PredictResult)
|
||||
assert result.mode == "baseline"
|
||||
assert result.provider == "baseline"
|
||||
@@ -144,15 +167,31 @@ def test_predict_route_has_no_dict_branch():
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 4. _persist_baseline 属性映射(baseline 落库语义不变)
|
||||
# 4. P3-2:baseline 服务层落库属性映射(落库已从路由移到 baseline.py)
|
||||
# ============================================================
|
||||
|
||||
|
||||
class _FakeResult:
|
||||
"""支持 .scalar_one_or_none() 的最小假结果集。"""
|
||||
|
||||
def __init__(self, items):
|
||||
self._items = list(items)
|
||||
|
||||
def scalars(self):
|
||||
return self
|
||||
|
||||
def all(self):
|
||||
return self._items
|
||||
|
||||
def scalar_one_or_none(self):
|
||||
return self._items[0] if self._items else None
|
||||
|
||||
|
||||
class _FakeUoW:
|
||||
"""替代 get_uow 的最小上下文管理器。"""
|
||||
"""替代 get_uow 的最小上下文管理器(session.execute 是 async 的)。"""
|
||||
|
||||
def __init__(self):
|
||||
self.session = SimpleNamespace()
|
||||
self.session = _make_session()
|
||||
|
||||
async def __aenter__(self):
|
||||
return self.session
|
||||
@@ -160,44 +199,67 @@ class _FakeUoW:
|
||||
async def __aexit__(self, *a):
|
||||
return None
|
||||
|
||||
def __call__(self):
|
||||
return self
|
||||
|
||||
|
||||
def _make_session(existing=None):
|
||||
"""构造带 async execute / add / flush 的假 session。"""
|
||||
sess = SimpleNamespace()
|
||||
|
||||
async def execute(*a, **k):
|
||||
return _FakeResult(existing or [])
|
||||
|
||||
sess.execute = execute
|
||||
sess.add = lambda *a, **k: None
|
||||
|
||||
async def flush(*a, **k):
|
||||
return None
|
||||
|
||||
sess.flush = flush
|
||||
return sess
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_persist_baseline_maps_attributes(monkeypatch):
|
||||
async def test_baseline_service_persists_with_correct_attributes(monkeypatch):
|
||||
"""P3-2:baseline 在服务层(predict_baseline)落库,属性映射与路由旧版一致。"""
|
||||
captured = {}
|
||||
|
||||
async def fake_upsert(session, **kwargs):
|
||||
captured.update(kwargs)
|
||||
return SimpleNamespace(id=77)
|
||||
|
||||
class FakeMatch:
|
||||
id = 1
|
||||
home_team_id = 10
|
||||
away_team_id = 20
|
||||
league_id = 1
|
||||
match_status = "scheduled"
|
||||
|
||||
class FakeSession:
|
||||
async def get(self, cls, mid):
|
||||
return FakeMatch()
|
||||
|
||||
class FakeSLC:
|
||||
async def __aenter__(self):
|
||||
return FakeSession()
|
||||
|
||||
async def __aexit__(self, *a):
|
||||
return None
|
||||
|
||||
async def fake_avg(db, *, team_id, side, league_id, before):
|
||||
return 2.0 if side == "home" else 1.0
|
||||
|
||||
monkeypatch.setattr("src.llm.baseline._avg_goals", fake_avg)
|
||||
monkeypatch.setattr("src.llm.baseline.AsyncSessionLocal", FakeSLC)
|
||||
monkeypatch.setattr("src.db.unit_of_work.get_uow", lambda: _FakeUoW())
|
||||
monkeypatch.setattr("src.llm.predict._upsert_prediction", fake_upsert)
|
||||
# baseline.py 模块级 import _upsert_prediction(第 15 行),需 patch baseline 模块属性
|
||||
monkeypatch.setattr("src.llm.baseline._upsert_prediction", fake_upsert)
|
||||
|
||||
from src.api.routes.predict import _persist_baseline
|
||||
result = await predict_baseline(1)
|
||||
|
||||
baseline = PredictResult(
|
||||
prediction_id=0, # baseline 不在服务层落库,由 _persist_baseline 落库后取得真实 id
|
||||
provider="baseline",
|
||||
model="baseline",
|
||||
prompt_version="baseline_v1",
|
||||
mode="baseline",
|
||||
pred_home_goals=2.0,
|
||||
pred_away_goals=1.0,
|
||||
alt_pred_home_goals=None,
|
||||
alt_pred_away_goals=None,
|
||||
pred_1x2="1",
|
||||
subjective_confidence=0.5,
|
||||
reasoning="r",
|
||||
context="",
|
||||
status="success",
|
||||
latency_ms=0,
|
||||
prompt_tokens=0,
|
||||
completion_tokens=0,
|
||||
raw={"home_avg": 2.1, "away_avg": 1.4},
|
||||
)
|
||||
|
||||
pid = await _persist_baseline(1, baseline)
|
||||
|
||||
assert pid == 77
|
||||
# 落库被调用且属性映射正确
|
||||
assert captured, f"predict_baseline 应调用 _upsert_prediction 落库,但 captured 为空(result.prediction_id={result.prediction_id!r})"
|
||||
assert captured["match_id"] == 1
|
||||
assert captured["provider_name"] == "baseline"
|
||||
assert captured["model"] == "baseline"
|
||||
@@ -212,5 +274,28 @@ async def test_persist_baseline_maps_attributes(monkeypatch):
|
||||
assert v["prompt_tokens"] == 0
|
||||
assert v["completion_tokens"] == 0
|
||||
assert v["latency_ms"] == 0
|
||||
assert v["raw_response"] == {"home_avg": 2.1, "away_avg": 1.4}
|
||||
assert v["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
|
||||
assert v["status"] == "success"
|
||||
|
||||
# 回填真实 prediction_id(服务层落库后取得)
|
||||
assert result.prediction_id == 77
|
||||
assert result.pred_1x2 == "1"
|
||||
assert captured["match_id"] == 1
|
||||
assert captured["provider_name"] == "baseline"
|
||||
assert captured["model"] == "baseline"
|
||||
assert captured["mode"] == "baseline"
|
||||
assert captured["run_type"] == "live"
|
||||
v = captured["values"]
|
||||
assert v["prompt_version"] == "baseline_v1"
|
||||
assert v["pred_home_goals"] == 2.0
|
||||
assert v["pred_away_goals"] == 1.0
|
||||
assert v["pred_1x2"] == "1"
|
||||
assert v["subjective_confidence"] == 0.5
|
||||
assert v["prompt_tokens"] == 0
|
||||
assert v["completion_tokens"] == 0
|
||||
assert v["latency_ms"] == 0
|
||||
assert v["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
|
||||
assert v["status"] == "success"
|
||||
|
||||
# 回填真实 prediction_id(服务层落库后取得)
|
||||
assert result.prediction_id == 77
|
||||
|
||||
Reference in New Issue
Block a user